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JWSoh / MZSR

Meta-Transfer Learning for Zero-Shot Super-Resolution (CVPR, 2020)
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Can Zero Shot learning methods be used when there is no GT available? #4

Open akhil451 opened 4 years ago

akhil451 commented 4 years ago

My question is that I have images where I need to perform super resolution, but do not have the high-resolution ground truth for them. How can I perform inference on these images?

JWSoh commented 4 years ago

Yes, of course. Ground-truth image is just for measuring PSNR. You may modify the code, where the PSNR is measured, or you can just feed an arbitrary image with the same size of expected high-resolution image.

BassantTolba1234 commented 3 years ago

Dear Sir, Amazing work ! Congratulation!! please , I have a question.can you kindly provide me with the full path I should insert of checkpoint the trained large scale training model to be able to use it as a pre-trained to meta transfer training? I'm waiting for your reply. Thanks in advance

BassantTolba1234 commented 3 years ago

Please @akhil451 I'm facing a problem when i load the pretrained model , specially when it reads the checkpoint this is the error .. how did you kindly solve it please ??

NotFoundError (see above for traceback): Key MODEL/conv7/kernel/Adam_3 not found in checkpoint [[Node: save/RestoreV2_69 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/device:CPU:0"](_arg_save/Const_0_0, save/RestoreV2_69/tensor_names, save/RestoreV2_69/shape_and_slices)]]

BassantTolba1234 commented 3 years ago

Please @JWSoh I'm facing a problem when i load the pretrained model , specially when it reads the checkpoint this is the error .. how did you kindly solve it please ??

NotFoundError (see above for traceback): Key MODEL/conv7/kernel/Adam_3 not found in checkpoint [[Node: save/RestoreV2_69 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/device:CPU:0"](_arg_save/Const_0_0, save/RestoreV2_69/tensor_names, save/RestoreV2_69/shape_and_slices)]]

BassantTolba1234 commented 3 years ago

Please can you kindly explain me how to calculate this weight loss ?

def get_loss_weights(self): loss_weights = tf.ones(shape=[self.TASK_ITER]) * (1.0/self.TASK_ITER) decay_rate = 1.0 / self.TASK_ITER / (10000 / 3) min_value= 0.03 / self.TASK_ITER

    loss_weights_pre = tf.maximum(loss_weights[:-1] - (tf.multiply(tf.to_float(self.global_step), decay_rate)), min_value)

    loss_weight_cur= tf.minimum(loss_weights[-1] + (tf.multiply(tf.to_float(self.global_step),(self.TASK_ITER- 1) * decay_rate)), 1.0 - ((self.TASK_ITER - 1) * min_value))
    loss_weights = tf.concat([[loss_weights_pre], [[loss_weight_cur]]], axis=1)
    return loss_weights